Norman is the VP of Geospatial at TileDB. Prior to joining TileDB, Norman focused on spatial indexing and image processing, and held engineering positions at Cloudant, IBM and Mapbox. He has a master's degree in Mathematics from the University of Durham, England.
We should show our SONAR data the same love and attention as the rest of our point clouds. SONAR suffers from the same challenges of scale, datasets split across thousands of files, and inability to quickly manipulate data. And yet, SONAR is one of the most interesting point cloud data types that should be analyzed at cloud scale, from finding shipwrecks to knowing whether a vessel is going to block the Suez Canal. This is where TileDB Embedded can help as an open-source library and cloud-native data engine for working with large multi-dimensional arrays.
TileDB Embedded can help accelerate SONAR analysis workflows in several ways. First, I will cover how analysis-ready TileDB arrays of many TBs can be sliced directly from cloud object storage in seconds, returning dataframes that are easily accessible from Pandas in Jupyter notebooks. Next, I will present TileDB integrations with familiar SONAR and point cloud tools like MB-System and PDAL, and how TileDB can help apply this information with modern data science techniques.
Finally, I will show real use-cases of TileDB with SONAR point clouds. With TileDB, you can avoid downloading full datasets and working across several domain-specific libraries. TileDB allows you to efficiently extract specific features and points of interest. The talk will conclude by addressing these challenges with a demo of subsea point cloud analysis.